Syntactic Boostrapping MA
Data V2
Anjie Cao and Molly Lewis
2020-07-29
DATA_PATH <- here("data/processed/syntactic_bootstrapping_tidy_data.csv") # make all variables (i.e. things that might change) as capital letters at the top of the scripts
ma_data <- read_csv(DATA_PATH)
ma_data <- ma_data %>% filter(paradigm_type == "action_matching")Data Overview
action
n_effect_sizes <- ma_data %>%
filter(!is.na(d_calc)) %>%
nrow()
n_papers <- ma_data %>%
distinct(unique_id) %>%
nrow()There are 79 effect sizes collected from 22 different papers.
Here are the papers in this analysis:
ma_data %>%
count(short_cite) %>%
arrange(-n) %>%
DT::datatable()ma_model <- rma(ma_data$d_calc, ma_data$d_var_calc)
ma_model##
## Random-Effects Model (k = 79; tau^2 estimator: REML)
##
## tau^2 (estimated amount of total heterogeneity): 0.6446 (SE = 0.1243)
## tau (square root of estimated tau^2 value): 0.8029
## I^2 (total heterogeneity / total variability): 88.11%
## H^2 (total variability / sampling variability): 8.41
##
## Test for Heterogeneity:
## Q(df = 78) = 409.5470, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## 0.5414 0.0999 5.4175 <.0001 0.3456 0.7373 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1forest(ma_model,
header = T,
slab = ma_data$unique_id,
col = "red",
cex = .7)
#funnel plot {.tabset} ## vanilla
ma_data %>%
mutate(color = ifelse(sentence_structure == "transitive", "red", "blue"),
color_sample_size = ifelse(n_1 < 10, "red", ifelse(n_1 < 20, "orange", "yellow")),
color_confidence = ifelse(inclusion_certainty == 1, "red", "black"))-> ma_data_funnel
ma_data_funnel %>% filter (abs(d_calc) < 5) -> ma_data_funnel_no_outlier
ss_colors <- ma_data_funnel$color
ss_colors_no_outlier <- ma_data_funnel_no_outlier$color
ma_model_funnel <- rma(ma_data_funnel$d_calc, ma_data_funnel$d_var_calc)
ma_model_funnel_no_outlier <- rma(ma_data_funnel_no_outlier$d_calc, ma_data_funnel_no_outlier$d_var_calc)
f1<- funnel(ma_model_funnel, xlab = "Effect Size", col = ss_colors)
legend("topright",bg = "white",legend = c("transitive","intransitive"),pch=16,col=c("red", "blue"))
title(main = "All effect sizes break down by sentence structure")
f2<- funnel(ma_model_funnel_no_outlier, xlab = "Effect Size", col = ss_colors_no_outlier)
legend("topright",bg = "white",legend = c("transitive","intransitive"),pch=16,col=c("red", "blue"))
title(main = "effect sizes excluded outliers (abs <5) break down by sentence structure")
take int occount sentence structure?
ma_model_sentence_structure <- rma(ma_data_funnel$d_calc~ma_data_funnel$sentence_structure, ma_data_funnel$d_var_calc)
ma_model_no_outlier_ss <- rma(ma_data_funnel_no_outlier$d_calc~ma_data_funnel_no_outlier$sentence_structure, ma_data_funnel_no_outlier$d_var_calc)
f3 <- funnel(ma_model_sentence_structure, xlab = "effect size", col = ss_colors)
f3_b <- funnel(ma_model_no_outlier_ss, xlab = "effect size", col = ss_colors_no_outlier)
take int account sentence structure and age?
ma_model_ss_age <- rma(ma_data_funnel$d_calc~ma_data_funnel$sentence_structure + ma_data_funnel $mean_age, ma_data_funnel$d_var_calc)
ma_model_funnel_no_outlier_ss_age <- rma(ma_data_funnel_no_outlier$d_calc~ma_data_funnel_no_outlier$sentence_structure+ma_data_funnel_no_outlier$mean_age, ma_data_funnel_no_outlier$d_var_calc)
f4 <- funnel(ma_model_ss_age, xlab = "effect size", col = ss_colors)
f4_b <- funnel(ma_model_funnel_no_outlier_ss_age, xlab = "effect size", col = ss_colors_no_outlier)
Variable Summary
Continuous Variables
CONTINUOUS_VARS <- c("n_1", "x_1", "sd_1", "d_calc", "d_var_calc", "mean_age")
long_continuous <- ma_data %>%
pivot_longer(cols = CONTINUOUS_VARS)
long_continuous %>%
ggplot(aes(x = value)) +
geom_histogram() +
facet_wrap(~ name, scale = "free_x") +
labs(title = "Distribution of continuous measures")
long_continuous %>%
group_by(name) %>%
summarize(mean = mean(value),
sd = sd(value)) %>%
kable()| name | mean | sd |
|---|---|---|
| d_calc | 0.5577306 | 1.4076258 |
| d_var_calc | 0.2043085 | 0.3694061 |
| mean_age | 906.7969722 | 338.3888715 |
| n_1 | 14.8734177 | 6.1880097 |
| sd_1 | 0.1417692 | 0.0751996 |
| x_1 | 0.5659904 | 0.0950815 |
Categorical Variables
CATEGORICAL_VARS <- c("sentence_structure", "agent_argument_type", "patient_argument_type", "stimuli_actor",
"presentation_type", "character_identification",
"test_mass_or_distributed", "practice_phase", "test_method")
long_categorical <- ma_data %>%
pivot_longer(cols = CATEGORICAL_VARS) %>%
count(name, value) # this is a short cut for group_by() %>% summarize(count = n())
long_categorical %>%
ggplot(aes(x = value, y = n)) +
facet_wrap(~ name, scale = "free_x") +
geom_col(position = 'dodge',width=0.4) +
theme(text = element_text(size=8),
axis.text.x = element_text(angle = 90, hjust = 1)) # rotate x-axis text
Explore Moderators
no moderators
all
m1 <- rma.mv(d_calc, V = d_var_calc,
random = ~ 1 | short_cite, data = ma_data)
summary(m1)##
## Multivariate Meta-Analysis Model (k = 79; method: REML)
##
## logLik Deviance AIC BIC AICc
## -163.3487 326.6973 330.6973 335.4107 330.8573
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2 0.2147 0.4634 22 no short_cite
##
## Test for Heterogeneity:
## Q(df = 78) = 409.5470, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## 0.3843 0.1080 3.5574 0.0004 0.1726 0.5961 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1<36
ma_data_young <- ma_data %>%
mutate(age_months = mean_age/30.44) %>%
filter(age_months < 36)
m_young <- rma.mv(d_calc, V = d_var_calc,
random = ~ 1 | short_cite, data = ma_data_young)
summary(m_young)##
## Multivariate Meta-Analysis Model (k = 57; method: REML)
##
## logLik Deviance AIC BIC AICc
## -115.7270 231.4539 235.4539 239.5046 235.6803
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2 0.2940 0.5422 17 no short_cite
##
## Test for Heterogeneity:
## Q(df = 56) = 302.2290, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## 0.4071 0.1414 2.8791 0.0040 0.1299 0.6842 **
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1>= 36
ma_data_old <- ma_data %>%
mutate(age_months = mean_age/30.44) %>%
filter(age_months > 36 | age_months == 36)
m_old <- rma.mv(d_calc, V = d_var_calc,
random = ~ 1 | short_cite, data = ma_data_old)
summary(m_old)##
## Multivariate Meta-Analysis Model (k = 22; method: REML)
##
## logLik Deviance AIC BIC AICc
## -42.9215 85.8431 89.8431 91.9321 90.5098
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2 0.0278 0.1668 6 no short_cite
##
## Test for Heterogeneity:
## Q(df = 21) = 107.2915, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## 0.3074 0.0947 3.2448 0.0012 0.1217 0.4931 **
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1age only
colored by unique_id
ma_data %>%
ggplot(aes(x = mean_age/30.44, y = d_calc,color = unique_id)) +
geom_point() +
ylab("Effect Size") +
xlab("Age (days)") +
ggtitle("Syntactical Bootstrapping effect size vs. Age (months)") 
all
ma_data %>%
ggplot(aes(x = mean_age/30.44, y = d_calc, size = n_1)) +
geom_point() +
geom_smooth(method = "lm") +
geom_smooth(color = "red") +
ylab("Effect Size") +
xlab("Age (days)") +
ggtitle("Syntactical Bootstrapping effect size vs. Age (months)") +
theme(legend.position = "none") 
m_simple <- rma.mv(d_calc ~ 1, V = d_var_calc,
random = ~ 1 | short_cite, data = ma_data)
m_age <- rma.mv(d_calc ~ mean_age, V = d_var_calc,
random = ~ 1 | short_cite, data = ma_data)
summary(m_simple)##
## Multivariate Meta-Analysis Model (k = 79; method: REML)
##
## logLik Deviance AIC BIC AICc
## -163.3487 326.6973 330.6973 335.4107 330.8573
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2 0.2147 0.4634 22 no short_cite
##
## Test for Heterogeneity:
## Q(df = 78) = 409.5470, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## 0.3843 0.1080 3.5574 0.0004 0.1726 0.5961 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1summary(m_age)##
## Multivariate Meta-Analysis Model (k = 79; method: REML)
##
## logLik Deviance AIC BIC AICc
## -159.9705 319.9409 325.9409 332.9723 326.2697
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2 0.2182 0.4671 22 no short_cite
##
## Test for Residual Heterogeneity:
## QE(df = 77) = 408.4665, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 6.2052, p-val = 0.0127
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.7840 0.1938 4.0458 <.0001 0.4042 1.1638 ***
## mean_age -0.0004 0.0002 -2.4910 0.0127 -0.0008 -0.0001 *
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1forest(m_age,
header = T,
slab = ma_data$unique_id,
col = "red",
cex = .7
)
funnel(m_age)
young, < 36
ma_data_young %>%
ggplot(aes(x = age_months, y = d_calc, size = n_1)) +
geom_point() +
geom_smooth(method = "lm") +
geom_smooth(color = "red") +
ylab("Effect Size") +
xlab("Age (days)") +
ggtitle("Syntactical Bootstrapping effect size vs. Age (days)") +
theme(legend.position = "none") 
m_age_young <- rma.mv(d_calc ~ mean_age, V = d_var_calc,
random = ~ 1 | short_cite, data = ma_data_young)
summary(m_age_young)##
## Multivariate Meta-Analysis Model (k = 57; method: REML)
##
## logLik Deviance AIC BIC AICc
## -115.3058 230.6116 236.6116 242.6336 237.0822
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2 0.2908 0.5393 17 no short_cite
##
## Test for Residual Heterogeneity:
## QE(df = 55) = 296.5941, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.3081, p-val = 0.5788
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.6132 0.3973 1.5433 0.1228 -0.1656 1.3920
## mean_age -0.0003 0.0005 -0.5551 0.5788 -0.0012 0.0007
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1forest(m_age_young,
header = T,
slab = ma_data_young$unique_id,
col = "red",
cex = .7
)
old, >= 36
ma_data_old %>%
ggplot(aes(x = age_months, y = d_calc, size = n_1)) +
geom_point() +
geom_smooth(method = "lm") +
geom_smooth(color = "red") +
ylab("Effect Size") +
xlab("Age (days)") +
ggtitle("Syntactical Bootstrapping effect size vs. Age (days)") +
theme(legend.position = "none") 
m_age_old <- rma.mv(d_calc ~ mean_age, V = d_var_calc,
random = ~ 1 | short_cite, data = ma_data_old)
summary(m_age_old)##
## Multivariate Meta-Analysis Model (k = 22; method: REML)
##
## logLik Deviance AIC BIC AICc
## -42.8041 85.6082 91.6082 94.5954 93.1082
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2 0.0322 0.1795 6 no short_cite
##
## Test for Residual Heterogeneity:
## QE(df = 20) = 107.1658, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.9219, p-val = 0.3370
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.7414 0.4639 1.5982 0.1100 -0.1679 1.6507
## mean_age -0.0003 0.0003 -0.9602 0.3370 -0.0010 0.0003
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1age and test type
all
ma_data %>%
mutate(age_months = mean_age/30.44) %>%
ggplot(aes(x = age_months, y = d_calc, size = n_1, color = test_method)) +
geom_point() +
geom_smooth(method = "lm") +
ylab("Effect Size") +
xlab("Age (days)") +
ggtitle("Syntactical Bootstrapping effect size vs. Age (days), breakdown by method") 
m_age_method <- rma.mv(d_calc ~ mean_age + test_method, V = d_var_calc,
random = ~ 1 | short_cite, data = ma_data)
summary(m_age_method)##
## Multivariate Meta-Analysis Model (k = 79; method: REML)
##
## logLik Deviance AIC BIC AICc
## -158.3123 316.6246 324.6246 333.9475 325.1880
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2 0.2008 0.4481 22 no short_cite
##
## Test for Residual Heterogeneity:
## QE(df = 76) = 392.3227, p-val < .0001
##
## Test of Moderators (coefficients 2:3):
## QM(df = 2) = 8.5127, p-val = 0.0142
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.7753 0.1910 4.0591 <.0001 0.4010 1.1497 ***
## mean_age -0.0005 0.0002 -2.8250 0.0047 -0.0009 -0.0002 **
## test_methodpoint 0.4311 0.2834 1.5213 0.1282 -0.1243 0.9865
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1age and sentence structure
all
ma_data %>%
mutate(age_months = mean_age/30.44) %>%
ggplot(aes(x = age_months, y = d_calc, size = n_1, , color = sentence_structure)) +
geom_point() +
geom_smooth(method = "lm") +
ylab("Effect Size") +
xlab("Age (days)") +
ggtitle("Syntactical Bootstrapping effect size vs. Age (days)") 
m_age_sentence <- rma.mv(d_calc ~ mean_age + sentence_structure, V = d_var_calc,
random = ~ 1 | short_cite, data = ma_data)
summary(m_age_sentence)##
## Multivariate Meta-Analysis Model (k = 79; method: REML)
##
## logLik Deviance AIC BIC AICc
## -152.4985 304.9970 312.9970 322.3200 313.5604
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2 0.2730 0.5225 22 no short_cite
##
## Test for Residual Heterogeneity:
## QE(df = 76) = 403.2276, p-val < .0001
##
## Test of Moderators (coefficients 2:3):
## QM(df = 2) = 21.7578, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb
## intrcpt 0.4870 0.2162 2.2527 0.0243 0.0633
## mean_age -0.0003 0.0002 -1.8192 0.0689 -0.0007
## sentence_structuretransitive 0.3208 0.0814 3.9422 <.0001 0.1613
## ci.ub
## intrcpt 0.9107 *
## mean_age 0.0000 .
## sentence_structuretransitive 0.4803 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Interaction:
m_age_sentence <- rma.mv(d_calc ~ mean_age * sentence_structure, V = d_var_calc,
random = ~ 1 | short_cite, data = ma_data)
summary(m_age_sentence)##
## Multivariate Meta-Analysis Model (k = 79; method: REML)
##
## logLik Deviance AIC BIC AICc
## -148.9253 297.8506 307.8506 319.4380 308.7201
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2 0.2577 0.5076 22 no short_cite
##
## Test for Residual Heterogeneity:
## QE(df = 75) = 383.4885, p-val < .0001
##
## Test of Moderators (coefficients 2:4):
## QM(df = 3) = 29.2563, p-val < .0001
##
## Model Results:
##
## estimate se zval pval
## intrcpt 0.7310 0.2312 3.1611 0.0016
## mean_age -0.0006 0.0002 -2.9344 0.0033
## sentence_structuretransitive -0.2585 0.2237 -1.1556 0.2478
## mean_age:sentence_structuretransitive 0.0006 0.0002 2.7696 0.0056
## ci.lb ci.ub
## intrcpt 0.2778 1.1842 **
## mean_age -0.0010 -0.0002 **
## sentence_structuretransitive -0.6968 0.1799
## mean_age:sentence_structuretransitive 0.0002 0.0011 **
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1< 36, young
ma_data_young_only <- ma_data %>%
mutate(age_months = mean_age/30.44) %>%
filter(age_months < 36)
ma_data_young_only %>%
filter(sentence_structure != "bare_verb") %>%
ggplot(aes(x = age_months, y = d_calc, size = n_1, , color = sentence_structure)) +
geom_point() +
geom_smooth(method = "lm") +
ylab("Effect Size") +
xlab("Age (days)") +
ggtitle("Syntactical Bootstrapping effect size vs. Age (days)") 
m_age_sentence_young <- rma.mv(d_calc ~ mean_age + sentence_structure, V = d_var_calc,
random = ~ 1 | short_cite, data = ma_data_young_only)
summary(m_age_sentence_young)##
## Multivariate Meta-Analysis Model (k = 57; method: REML)
##
## logLik Deviance AIC BIC AICc
## -114.4871 228.9742 236.9742 244.9301 237.7905
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2 0.3256 0.5706 17 no short_cite
##
## Test for Residual Heterogeneity:
## QE(df = 54) = 293.9865, p-val < .0001
##
## Test of Moderators (coefficients 2:3):
## QM(df = 2) = 2.2202, p-val = 0.3295
##
## Model Results:
##
## estimate se zval pval ci.lb
## intrcpt 0.4825 0.4118 1.1716 0.2413 -0.3247
## mean_age -0.0002 0.0005 -0.3992 0.6898 -0.0012
## sentence_structuretransitive 0.1316 0.0946 1.3915 0.1641 -0.0538
## ci.ub
## intrcpt 1.2897
## mean_age 0.0008
## sentence_structuretransitive 0.3169
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1interaction:
m_age_sentence_young <- rma.mv(d_calc ~ mean_age * sentence_structure, V = d_var_calc,
random = ~ 1 | short_cite, data = ma_data_young_only)
summary(m_age_sentence_young)##
## Multivariate Meta-Analysis Model (k = 57; method: REML)
##
## logLik Deviance AIC BIC AICc
## -111.3248 222.6496 232.6496 242.5010 233.9262
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2 0.3002 0.5479 17 no short_cite
##
## Test for Residual Heterogeneity:
## QE(df = 53) = 281.8953, p-val < .0001
##
## Test of Moderators (coefficients 2:4):
## QM(df = 3) = 8.8233, p-val = 0.0317
##
## Model Results:
##
## estimate se zval pval
## intrcpt 0.1283 0.4318 0.2971 0.7664
## mean_age 0.0003 0.0005 0.6080 0.5432
## sentence_structuretransitive 1.3145 0.4685 2.8058 0.0050
## mean_age:sentence_structuretransitive -0.0016 0.0006 -2.5857 0.0097
## ci.lb ci.ub
## intrcpt -0.7181 0.9747
## mean_age -0.0007 0.0014
## sentence_structuretransitive 0.3963 2.2328 **
## mean_age:sentence_structuretransitive -0.0029 -0.0004 **
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1>= 36, old
ma_data_old <- ma_data %>%
mutate(age_months = mean_age/30.44) %>%
filter(age_months > 36 | age_months == 36)
ma_data_old %>%
filter(sentence_structure != "bare_verb") %>%
ggplot(aes(x = age_months, y = d_calc, size = n_1, color = sentence_structure)) +
geom_point() +
geom_smooth(method = "lm") +
ylab("Effect Size") +
xlab("Age (days)") +
ggtitle("Syntactical Bootstrapping effect size vs. Age (days)") 
m_age_sentence_old <- rma.mv(d_calc ~ mean_age + sentence_structure, V = d_var_calc,
random = ~ 1 | short_cite, data = ma_data_old)
summary(m_age_sentence_old)##
## Multivariate Meta-Analysis Model (k = 22; method: REML)
##
## logLik Deviance AIC BIC AICc
## -27.4835 54.9669 62.9669 66.7447 65.8240
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2 0.0000 0.0000 6 no short_cite
##
## Test for Residual Heterogeneity:
## QE(df = 19) = 71.5803, p-val < .0001
##
## Test of Moderators (coefficients 2:3):
## QM(df = 2) = 35.7112, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb
## intrcpt 0.1217 0.4116 0.2958 0.7674 -0.6849
## mean_age -0.0002 0.0003 -0.5996 0.5488 -0.0008
## sentence_structuretransitive 0.6793 0.1139 5.9654 <.0001 0.4561
## ci.ub
## intrcpt 0.9284
## mean_age 0.0004
## sentence_structuretransitive 0.9025 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1interaction
m_age_sentence_old <- rma.mv(d_calc ~ mean_age * sentence_structure, V = d_var_calc,
random = ~ 1 | short_cite, data = ma_data_old)
summary(m_age_sentence_old)##
## Multivariate Meta-Analysis Model (k = 22; method: REML)
##
## logLik Deviance AIC BIC AICc
## -27.3891 54.7783 64.7783 69.2301 69.7783
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2 0.0000 0.0000 6 no short_cite
##
## Test for Residual Heterogeneity:
## QE(df = 18) = 70.3088, p-val < .0001
##
## Test of Moderators (coefficients 2:4):
## QM(df = 3) = 36.9827, p-val < .0001
##
## Model Results:
##
## estimate se zval pval
## intrcpt 0.9460 0.8389 1.1277 0.2595
## mean_age -0.0008 0.0006 -1.2770 0.2016
## sentence_structuretransitive -0.3953 0.9598 -0.4119 0.6804
## mean_age:sentence_structuretransitive 0.0008 0.0007 1.1276 0.2595
## ci.lb ci.ub
## intrcpt -0.6982 2.5902
## mean_age -0.0020 0.0004
## sentence_structuretransitive -2.2765 1.4858
## mean_age:sentence_structuretransitive -0.0006 0.0022
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1age with stimuli_actor
all
ma_data %>%
mutate(age_months = mean_age/30.44) %>%
ggplot(aes(x = age_months, y = d_calc, size = n_1, color = stimuli_actor)) +
geom_point() +
geom_smooth(method = "lm") +
ylab("Effect Size") +
xlab("Age (days)") +
ggtitle("Syntactical Bootstrapping effect size vs. Age (days), breakdown by stimuli_actor") 
m_age_stimuli_actor <- rma.mv(d_calc ~ mean_age + stimuli_actor, V = d_var_calc,
random = ~ 1 | short_cite, data = ma_data)
m_age_stimuli_actor_interaction <- rma.mv(d_calc ~ mean_age * stimuli_actor, V = d_var_calc,
random = ~ 1 | short_cite, data = ma_data)
summary(m_age_stimuli_actor)##
## Multivariate Meta-Analysis Model (k = 79; method: REML)
##
## logLik Deviance AIC BIC AICc
## -159.2579 318.5159 326.5159 335.8388 327.0793
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2 0.2247 0.4740 22 no short_cite
##
## Test for Residual Heterogeneity:
## QE(df = 76) = 402.2188, p-val < .0001
##
## Test of Moderators (coefficients 2:3):
## QM(df = 2) = 6.5734, p-val = 0.0374
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.7731 0.1958 3.9484 <.0001 0.3893 1.1568 ***
## mean_age -0.0004 0.0002 -1.6743 0.0941 -0.0008 0.0001 .
## stimuli_actorperson -0.0975 0.1612 -0.6051 0.5451 -0.4135 0.2184
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1summary(m_age_stimuli_actor_interaction)##
## Multivariate Meta-Analysis Model (k = 79; method: REML)
##
## logLik Deviance AIC BIC AICc
## -158.3652 316.7303 326.7303 338.3178 327.5999
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2 0.2204 0.4695 22 no short_cite
##
## Test for Residual Heterogeneity:
## QE(df = 75) = 393.4459, p-val < .0001
##
## Test of Moderators (coefficients 2:4):
## QM(df = 3) = 7.0701, p-val = 0.0697
##
## Model Results:
##
## estimate se zval pval ci.lb
## intrcpt 0.6344 0.2769 2.2913 0.0219 0.0917
## mean_age -0.0002 0.0003 -0.7799 0.4354 -0.0008
## stimuli_actorperson 0.2888 0.5721 0.5048 0.6137 -0.8325
## mean_age:stimuli_actorperson -0.0004 0.0006 -0.7034 0.4818 -0.0016
## ci.ub
## intrcpt 1.1771 *
## mean_age 0.0003
## stimuli_actorperson 1.4101
## mean_age:stimuli_actorperson 0.0007
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1<36, young
ma_data_young_only %>%
mutate(age_months = mean_age/30.44) %>%
ggplot(aes(x = age_months, y = d_calc, size = n_1, color = stimuli_actor)) +
geom_point() +
geom_smooth(method = "lm") +
ylab("Effect Size") +
xlab("Age (days)") +
ggtitle("Syntactical Bootstrapping effect size vs. Age (days), breakdown by stimuli_actor, young only") 
m_age_stimuli_actor_young <- rma.mv(d_calc ~ mean_age + stimuli_actor, V = d_var_calc,
random = ~ 1 | short_cite, data = ma_data_young_only)
m_age_stimuli_actor_interaction_young <- rma.mv(d_calc ~ mean_age * stimuli_actor, V = d_var_calc,
random = ~ 1 | short_cite, data = ma_data_young_only)
summary(m_age_stimuli_actor_young)##
## Multivariate Meta-Analysis Model (k = 57; method: REML)
##
## logLik Deviance AIC BIC AICc
## -114.4670 228.9340 236.9340 244.8899 237.7503
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2 0.3085 0.5554 17 no short_cite
##
## Test for Residual Heterogeneity:
## QE(df = 54) = 296.5893, p-val < .0001
##
## Test of Moderators (coefficients 2:3):
## QM(df = 2) = 0.3155, p-val = 0.8541
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.6312 0.4263 1.4808 0.1387 -0.2042 1.4667
## mean_age -0.0003 0.0006 -0.5047 0.6138 -0.0016 0.0009
## stimuli_actorperson 0.0387 0.2706 0.1429 0.8864 -0.4918 0.5691
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1summary(m_age_stimuli_actor_interaction_young)##
## Multivariate Meta-Analysis Model (k = 57; method: REML)
##
## logLik Deviance AIC BIC AICc
## -113.4216 226.8432 236.8432 246.6946 238.1198
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2 0.3492 0.5910 17 no short_cite
##
## Test for Residual Heterogeneity:
## QE(df = 53) = 290.5912, p-val < .0001
##
## Test of Moderators (coefficients 2:4):
## QM(df = 3) = 0.6425, p-val = 0.8866
##
## Model Results:
##
## estimate se zval pval ci.lb
## intrcpt 1.0220 0.7973 1.2819 0.1999 -0.5407
## mean_age -0.0008 0.0011 -0.7531 0.4514 -0.0030
## stimuli_actorperson -0.8348 1.4629 -0.5706 0.5683 -3.7021
## mean_age:stimuli_actorperson 0.0011 0.0019 0.6016 0.5474 -0.0026
## ci.ub
## intrcpt 2.5847
## mean_age 0.0013
## stimuli_actorperson 2.0325
## mean_age:stimuli_actorperson 0.0048
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1>= 36, old
ma_data_old %>%
mutate(age_months = mean_age/30.44) %>%
ggplot(aes(x = age_months, y = d_calc, size = n_1, color = stimuli_actor)) +
geom_point() +
ylab("Effect Size") +
xlab("Age (days)") +
ggtitle("Syntactical Bootstrapping effect size vs. Age (days), breakdown by stimuli_actor, old only") 
m_age_stimuli_actor_old <- rma.mv(d_calc ~ mean_age + stimuli_actor, V = d_var_calc,
random = ~ 1 | short_cite, data = ma_data_old)
m_age_stimuli_actor_interaction_old <- rma.mv(d_calc ~ mean_age * stimuli_actor, V = d_var_calc,
random = ~ 1 | short_cite, data = ma_data_old)
summary(m_age_stimuli_actor_old)##
## Multivariate Meta-Analysis Model (k = 22; method: REML)
##
## logLik Deviance AIC BIC AICc
## -39.5886 79.1771 87.1771 90.9549 90.0343
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2 0.0000 0.0000 6 no short_cite
##
## Test for Residual Heterogeneity:
## QE(df = 19) = 95.8680, p-val < .0001
##
## Test of Moderators (coefficients 2:3):
## QM(df = 2) = 11.4235, p-val = 0.0033
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.8306 0.4191 1.9818 0.0475 0.0092 1.6520 *
## mean_age -0.0003 0.0003 -0.9079 0.3639 -0.0009 0.0003
## stimuli_actorperson -0.4021 0.1196 -3.3612 0.0008 -0.6366 -0.1676 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1summary(m_age_stimuli_actor_interaction_old)##
## Multivariate Meta-Analysis Model (k = 22; method: REML)
##
## logLik Deviance AIC BIC AICc
## -39.9337 79.8673 89.8673 94.3192 94.8673
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2 0.0000 0.0000 6 no short_cite
##
## Test for Residual Heterogeneity:
## QE(df = 18) = 95.5949, p-val < .0001
##
## Test of Moderators (coefficients 2:4):
## QM(df = 3) = 11.6966, p-val = 0.0085
##
## Model Results:
##
## estimate se zval pval ci.lb
## intrcpt 0.8938 0.4362 2.0490 0.0405 0.0388
## mean_age -0.0003 0.0003 -1.0180 0.3087 -0.0009
## stimuli_actorperson -1.1721 1.4782 -0.7929 0.4278 -4.0693
## mean_age:stimuli_actorperson 0.0006 0.0011 0.5226 0.6013 -0.0016
## ci.ub
## intrcpt 1.7487 *
## mean_age 0.0003
## stimuli_actorperson 1.7251
## mean_age:stimuli_actorperson 0.0028
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1age with presentation_type
Decided to only compare asynchronous and immediate after:
ma_data %>%
count(presentation_type)## # A tibble: 3 x 2
## presentation_type n
## <chr> <int>
## 1 asynchronous 35
## 2 immediate_after 32
## 3 simultaneous 12ma_data_pt <- ma_data %>%
filter(presentation_type != "simultaneous")
ma_data_pt_young <- ma_data_pt %>%
mutate(age_months = mean_age/30.44) %>%
filter(age_months < 36)
ma_data_pt_old <- ma_data_pt %>%
mutate(age_months = mean_age/30.44) %>%
filter(age_months > 36 | age_months == 36) all
ma_data_pt %>%
mutate(age_months = mean_age/30.44) %>%
ggplot(aes(x = age_months, y = d_calc, size = n_1, color = presentation_type)) +
geom_point() +
geom_smooth(method = "lm") +
ylab("Effect Size") +
xlab("Age (days)") +
ggtitle("Syntactical Bootstrapping effect size vs. Age (days), breakdown by presentation type") 
m_age_pt <- rma.mv(d_calc ~ mean_age + presentation_type, V = d_var_calc,
random = ~ 1 | short_cite, data = ma_data_pt)
m_age_pt_interaction <- rma.mv(d_calc ~ mean_age * presentation_type, V = d_var_calc,
random = ~ 1 | short_cite, data = ma_data_pt)
summary(m_age_pt)##
## Multivariate Meta-Analysis Model (k = 67; method: REML)
##
## logLik Deviance AIC BIC AICc
## -117.7657 235.5315 243.5315 252.1670 244.2095
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2 0.3222 0.5676 14 no short_cite
##
## Test for Residual Heterogeneity:
## QE(df = 64) = 309.1098, p-val < .0001
##
## Test of Moderators (coefficients 2:3):
## QM(df = 2) = 6.5376, p-val = 0.0381
##
## Model Results:
##
## estimate se zval pval ci.lb
## intrcpt 0.8560 0.2336 3.6648 0.0002 0.3982
## mean_age -0.0005 0.0002 -2.5538 0.0107 -0.0008
## presentation_typeimmediate_after 0.0166 0.1889 0.0880 0.9299 -0.3537
## ci.ub
## intrcpt 1.3138 ***
## mean_age -0.0001 *
## presentation_typeimmediate_after 0.3869
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1summary(m_age_pt_interaction)##
## Multivariate Meta-Analysis Model (k = 67; method: REML)
##
## logLik Deviance AIC BIC AICc
## -117.0993 234.1987 244.1987 254.9144 245.2513
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2 0.3151 0.5613 14 no short_cite
##
## Test for Residual Heterogeneity:
## QE(df = 63) = 303.2844, p-val < .0001
##
## Test of Moderators (coefficients 2:4):
## QM(df = 3) = 7.6700, p-val = 0.0533
##
## Model Results:
##
## estimate se zval pval
## intrcpt 0.9282 0.2424 3.8293 0.0001
## mean_age -0.0006 0.0002 -2.7277 0.0064
## presentation_typeimmediate_after -0.2834 0.3412 -0.8305 0.4062
## mean_age:presentation_typeimmediate_after 0.0004 0.0004 1.0605 0.2889
## ci.lb ci.ub
## intrcpt 0.4531 1.4033 ***
## mean_age -0.0010 -0.0002 **
## presentation_typeimmediate_after -0.9522 0.3854
## mean_age:presentation_typeimmediate_after -0.0004 0.0012
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1<36, young
ma_data_pt_young %>%
mutate(age_months = mean_age/30.44) %>%
ggplot(aes(x = age_months, y = d_calc, size = n_1, color = presentation_type)) +
geom_point() +
geom_smooth(method = "lm") +
ylab("Effect Size") +
xlab("Age (days)") +
ggtitle("Syntactical Bootstrapping effect size vs. Age (days), breakdown by presentation type, young only") 
m_data_pt_young <- rma.mv(d_calc ~ mean_age + presentation_type, V = d_var_calc,
random = ~ 1 | short_cite, data = ma_data_pt_young)
m_age_pt_interaction_young <- rma.mv(d_calc ~ mean_age * presentation_type, V = d_var_calc,
random = ~ 1 | short_cite, data = ma_data_pt_young)
summary(m_age_pt)##
## Multivariate Meta-Analysis Model (k = 67; method: REML)
##
## logLik Deviance AIC BIC AICc
## -117.7657 235.5315 243.5315 252.1670 244.2095
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2 0.3222 0.5676 14 no short_cite
##
## Test for Residual Heterogeneity:
## QE(df = 64) = 309.1098, p-val < .0001
##
## Test of Moderators (coefficients 2:3):
## QM(df = 2) = 6.5376, p-val = 0.0381
##
## Model Results:
##
## estimate se zval pval ci.lb
## intrcpt 0.8560 0.2336 3.6648 0.0002 0.3982
## mean_age -0.0005 0.0002 -2.5538 0.0107 -0.0008
## presentation_typeimmediate_after 0.0166 0.1889 0.0880 0.9299 -0.3537
## ci.ub
## intrcpt 1.3138 ***
## mean_age -0.0001 *
## presentation_typeimmediate_after 0.3869
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1summary(m_age_pt_interaction)##
## Multivariate Meta-Analysis Model (k = 67; method: REML)
##
## logLik Deviance AIC BIC AICc
## -117.0993 234.1987 244.1987 254.9144 245.2513
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2 0.3151 0.5613 14 no short_cite
##
## Test for Residual Heterogeneity:
## QE(df = 63) = 303.2844, p-val < .0001
##
## Test of Moderators (coefficients 2:4):
## QM(df = 3) = 7.6700, p-val = 0.0533
##
## Model Results:
##
## estimate se zval pval
## intrcpt 0.9282 0.2424 3.8293 0.0001
## mean_age -0.0006 0.0002 -2.7277 0.0064
## presentation_typeimmediate_after -0.2834 0.3412 -0.8305 0.4062
## mean_age:presentation_typeimmediate_after 0.0004 0.0004 1.0605 0.2889
## ci.lb ci.ub
## intrcpt 0.4531 1.4033 ***
## mean_age -0.0010 -0.0002 **
## presentation_typeimmediate_after -0.9522 0.3854
## mean_age:presentation_typeimmediate_after -0.0004 0.0012
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1>= 36, old
ma_data_pt_old %>%
mutate(age_months = mean_age/30.44) %>%
ggplot(aes(x = age_months, y = d_calc, size = n_1, color = presentation_type)) +
geom_point() +
geom_smooth(method = "lm") +
ylab("Effect Size") +
xlab("Age (days)") +
ggtitle("Syntactical Bootstrapping effect size vs. Age (days), breakdown by presentation type, young only") 
age with character_identification
all
ma_data %>%
mutate(age_months = mean_age/30.44) %>%
ggplot(aes(x = age_months, y = d_calc, size = n_1, color = character_identification)) +
geom_point() +
geom_smooth(method = "lm") +
ylab("Effect Size") +
xlab("Age (days)") +
ggtitle("Syntactical Bootstrapping effect size vs. Age (days), breakdown by character_identification") 
m_age_ci <- rma.mv(d_calc ~ mean_age + character_identification, V = d_var_calc,
random = ~ 1 | short_cite, data = ma_data)
m_age_stimuli_ci_interaction <- rma.mv(d_calc ~ mean_age * character_identification, V = d_var_calc,
random = ~ 1 | short_cite, data = ma_data)
summary(m_age_ci)##
## Multivariate Meta-Analysis Model (k = 79; method: REML)
##
## logLik Deviance AIC BIC AICc
## -159.0126 318.0251 326.0251 335.3481 326.5885
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2 0.2252 0.4745 22 no short_cite
##
## Test for Residual Heterogeneity:
## QE(df = 76) = 401.9504, p-val < .0001
##
## Test of Moderators (coefficients 2:3):
## QM(df = 2) = 6.6079, p-val = 0.0367
##
## Model Results:
##
## estimate se zval pval ci.lb
## intrcpt 0.7360 0.2098 3.5078 0.0005 0.3247
## mean_age -0.0005 0.0002 -2.5629 0.0104 -0.0008
## character_identificationyes 0.1418 0.2241 0.6328 0.5269 -0.2975
## ci.ub
## intrcpt 1.1472 ***
## mean_age -0.0001 *
## character_identificationyes 0.5811
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1summary(m_age_stimuli_ci_interaction)##
## Multivariate Meta-Analysis Model (k = 79; method: REML)
##
## logLik Deviance AIC BIC AICc
## -158.5201 317.0401 327.0401 338.6276 327.9097
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2 0.2271 0.4765 22 no short_cite
##
## Test for Residual Heterogeneity:
## QE(df = 75) = 396.8563, p-val < .0001
##
## Test of Moderators (coefficients 2:4):
## QM(df = 3) = 7.3253, p-val = 0.0622
##
## Model Results:
##
## estimate se zval pval
## intrcpt 0.8207 0.2326 3.5280 0.0004
## mean_age -0.0006 0.0002 -2.5931 0.0095
## character_identificationyes -0.1696 0.4303 -0.3942 0.6934
## mean_age:character_identificationyes 0.0003 0.0004 0.8487 0.3960
## ci.lb ci.ub
## intrcpt 0.3648 1.2767 ***
## mean_age -0.0010 -0.0001 **
## character_identificationyes -1.0130 0.6738
## mean_age:character_identificationyes -0.0004 0.0011
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1<36, young
ma_data_young_only %>%
mutate(age_months = mean_age/30.44) %>%
ggplot(aes(x = age_months, y = d_calc, size = n_1, color = character_identification)) +
geom_point() +
geom_smooth(method = "lm") +
ylab("Effect Size") +
xlab("Age (days)") +
ggtitle("Syntactical Bootstrapping effect size vs. Age (days), breakdown by character_identification, young only") 
m_age_ci_young <- rma.mv(d_calc ~ mean_age + character_identification, V = d_var_calc,
random = ~ 1 | short_cite, data = ma_data_young_only)
m_age_ci_interaction_young <- rma.mv(d_calc ~ mean_age * character_identification, V = d_var_calc,
random = ~ 1 | short_cite, data = ma_data_young_only)
summary(m_age_ci_young)##
## Multivariate Meta-Analysis Model (k = 57; method: REML)
##
## logLik Deviance AIC BIC AICc
## -114.3668 228.7337 236.7337 244.6896 237.5500
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2 0.3144 0.5607 17 no short_cite
##
## Test for Residual Heterogeneity:
## QE(df = 54) = 294.3994, p-val < .0001
##
## Test of Moderators (coefficients 2:3):
## QM(df = 2) = 0.2973, p-val = 0.8619
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.6038 0.4060 1.4874 0.1369 -0.1919 1.3995
## mean_age -0.0003 0.0005 -0.5450 0.5857 -0.0012 0.0007
## character_identificationyes 0.0241 0.3052 0.0791 0.9369 -0.5740 0.6223
##
## intrcpt
## mean_age
## character_identificationyes
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1summary(m_age_ci_interaction_young)##
## Multivariate Meta-Analysis Model (k = 57; method: REML)
##
## logLik Deviance AIC BIC AICc
## -113.5404 227.0809 237.0809 246.9323 238.3575
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2 0.3441 0.5866 17 no short_cite
##
## Test for Residual Heterogeneity:
## QE(df = 53) = 291.6547, p-val < .0001
##
## Test of Moderators (coefficients 2:4):
## QM(df = 3) = 0.6826, p-val = 0.8773
##
## Model Results:
##
## estimate se zval pval
## intrcpt 0.6697 0.4253 1.5747 0.1153
## mean_age -0.0004 0.0005 -0.6893 0.4906
## character_identificationyes -0.8853 1.4630 -0.6051 0.5451
## mean_age:character_identificationyes 0.0011 0.0018 0.6359 0.5249
## ci.lb ci.ub
## intrcpt -0.1638 1.5032
## mean_age -0.0014 0.0007
## character_identificationyes -3.7528 1.9823
## mean_age:character_identificationyes -0.0023 0.0046
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1>= 36, old
ma_data_old %>%
mutate(age_months = mean_age/30.44) %>%
ggplot(aes(x = age_months, y = d_calc, size = n_1, color = character_identification)) +
geom_point() +
ylab("Effect Size") +
xlab("Age (days)") +
ggtitle("Syntactical Bootstrapping effect size vs. Age (days), breakdown by character_identification, old only") 
age with practice_phase
all
ma_data %>%
mutate(age_months = mean_age/30.44) %>%
ggplot(aes(x = age_months, y = d_calc, size = n_1, color = practice_phase)) +
geom_point() +
geom_smooth(method = "lm") +
ylab("Effect Size") +
xlab("Age (days)") +
ggtitle("Syntactical Bootstrapping effect size vs. Age (days), breakdown by practice_phase") 
m_age_pf <- rma.mv(d_calc ~ mean_age + character_identification, V = d_var_calc,
random = ~ 1 | short_cite, data = ma_data)
m_age_pf_interaction <- rma.mv(d_calc ~ mean_age * character_identification, V = d_var_calc,
random = ~ 1 | short_cite, data = ma_data)
summary(m_age_ci)##
## Multivariate Meta-Analysis Model (k = 79; method: REML)
##
## logLik Deviance AIC BIC AICc
## -159.0126 318.0251 326.0251 335.3481 326.5885
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2 0.2252 0.4745 22 no short_cite
##
## Test for Residual Heterogeneity:
## QE(df = 76) = 401.9504, p-val < .0001
##
## Test of Moderators (coefficients 2:3):
## QM(df = 2) = 6.6079, p-val = 0.0367
##
## Model Results:
##
## estimate se zval pval ci.lb
## intrcpt 0.7360 0.2098 3.5078 0.0005 0.3247
## mean_age -0.0005 0.0002 -2.5629 0.0104 -0.0008
## character_identificationyes 0.1418 0.2241 0.6328 0.5269 -0.2975
## ci.ub
## intrcpt 1.1472 ***
## mean_age -0.0001 *
## character_identificationyes 0.5811
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1summary(m_age_stimuli_ci_interaction)##
## Multivariate Meta-Analysis Model (k = 79; method: REML)
##
## logLik Deviance AIC BIC AICc
## -158.5201 317.0401 327.0401 338.6276 327.9097
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2 0.2271 0.4765 22 no short_cite
##
## Test for Residual Heterogeneity:
## QE(df = 75) = 396.8563, p-val < .0001
##
## Test of Moderators (coefficients 2:4):
## QM(df = 3) = 7.3253, p-val = 0.0622
##
## Model Results:
##
## estimate se zval pval
## intrcpt 0.8207 0.2326 3.5280 0.0004
## mean_age -0.0006 0.0002 -2.5931 0.0095
## character_identificationyes -0.1696 0.4303 -0.3942 0.6934
## mean_age:character_identificationyes 0.0003 0.0004 0.8487 0.3960
## ci.lb ci.ub
## intrcpt 0.3648 1.2767 ***
## mean_age -0.0010 -0.0001 **
## character_identificationyes -1.0130 0.6738
## mean_age:character_identificationyes -0.0004 0.0011
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1<36, young
ma_data_young_only %>%
mutate(age_months = mean_age/30.44) %>%
ggplot(aes(x = age_months, y = d_calc, size = n_1, color = practice_phase)) +
geom_point() +
geom_smooth(method = "lm") +
ylab("Effect Size") +
xlab("Age (days)") +
ggtitle("Syntactical Bootstrapping effect size vs. Age (days), breakdown by practice_phase, young only") 
m_age_pf_young <- rma.mv(d_calc ~ mean_age + practice_phase, V = d_var_calc,
random = ~ 1 | short_cite, data = ma_data_young_only)
m_age_pf_interaction_young <- rma.mv(d_calc ~ mean_age * practice_phase, V = d_var_calc,
random = ~ 1 | short_cite, data = ma_data_young_only)
summary(m_age_pf_young)##
## Multivariate Meta-Analysis Model (k = 57; method: REML)
##
## logLik Deviance AIC BIC AICc
## -114.5234 229.0468 237.0468 245.0027 237.8631
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2 0.2749 0.5243 17 no short_cite
##
## Test for Residual Heterogeneity:
## QE(df = 54) = 290.5875, p-val < .0001
##
## Test of Moderators (coefficients 2:3):
## QM(df = 2) = 0.8004, p-val = 0.6702
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.7492 0.4393 1.7055 0.0881 -0.1118 1.6103 .
## mean_age -0.0005 0.0006 -0.8686 0.3851 -0.0017 0.0007
## practice_phaseyes 0.1335 0.1928 0.6921 0.4889 -0.2445 0.5114
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1summary(m_age_pf_interaction_young)##
## Multivariate Meta-Analysis Model (k = 57; method: REML)
##
## logLik Deviance AIC BIC AICc
## -113.3324 226.6648 236.6648 246.5162 237.9414
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2 0.3045 0.5518 17 no short_cite
##
## Test for Residual Heterogeneity:
## QE(df = 53) = 288.4535, p-val < .0001
##
## Test of Moderators (coefficients 2:4):
## QM(df = 3) = 1.9467, p-val = 0.5835
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 1.3243 0.6888 1.9225 0.0545 -0.0258 2.6743
## mean_age -0.0014 0.0010 -1.3847 0.1662 -0.0033 0.0006
## practice_phaseyes -0.9787 0.9969 -0.9817 0.3263 -2.9326 0.9753
## mean_age:practice_phaseyes 0.0016 0.0014 1.1256 0.2603 -0.0012 0.0044
##
## intrcpt .
## mean_age
## practice_phaseyes
## mean_age:practice_phaseyes
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1>= 36, old
ma_data_old %>%
mutate(age_months = mean_age/30.44) %>%
ggplot(aes(x = age_months, y = d_calc, size = n_1, color = practice_phase)) +
geom_point() +
ylab("Effect Size") +
xlab("Age (days)") +
ggtitle("Syntactical Bootstrapping effect size vs. Age (days), breakdown by practice_phase, old only") 
age with test_mass_or_distributed
all
ma_data %>%
mutate(age_months = mean_age/30.44) %>%
ggplot(aes(x = age_months, y = d_calc, size = n_1, color = test_mass_or_distributed)) +
geom_point() +
geom_smooth(method = "lm") +
ylab("Effect Size") +
xlab("Age (days)") +
ggtitle("Syntactical Bootstrapping effect size vs. Age (days), breakdown by test type") 
m_age_tt <- rma.mv(d_calc ~ mean_age + test_mass_or_distributed, V = d_var_calc,
random = ~ 1 | short_cite, data = ma_data)
m_age_tt_interaction <- rma.mv(d_calc ~ mean_age * test_mass_or_distributed, V = d_var_calc,
random = ~ 1 | short_cite, data = ma_data)
summary(m_age_tt)##
## Multivariate Meta-Analysis Model (k = 79; method: REML)
##
## logLik Deviance AIC BIC AICc
## -159.0278 318.0556 326.0556 335.3786 326.6190
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2 0.2317 0.4813 22 no short_cite
##
## Test for Residual Heterogeneity:
## QE(df = 76) = 407.4472, p-val < .0001
##
## Test of Moderators (coefficients 2:3):
## QM(df = 2) = 6.2337, p-val = 0.0443
##
## Model Results:
##
## estimate se zval pval ci.lb
## intrcpt 0.7975 0.2088 3.8202 0.0001 0.3883
## mean_age -0.0004 0.0002 -2.4952 0.0126 -0.0008
## test_mass_or_distributedmass -0.0393 0.2515 -0.1562 0.8759 -0.5323
## ci.ub
## intrcpt 1.2067 ***
## mean_age -0.0001 *
## test_mass_or_distributedmass 0.4537
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1summary(m_age_tt_interaction)##
## Multivariate Meta-Analysis Model (k = 79; method: REML)
##
## logLik Deviance AIC BIC AICc
## -158.2020 316.4039 326.4039 337.9913 327.2735
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2 0.2271 0.4766 22 no short_cite
##
## Test for Residual Heterogeneity:
## QE(df = 75) = 397.5932, p-val < .0001
##
## Test of Moderators (coefficients 2:4):
## QM(df = 3) = 7.4416, p-val = 0.0591
##
## Model Results:
##
## estimate se zval pval
## intrcpt 0.5393 0.3133 1.7215 0.0852
## mean_age -0.0002 0.0003 -0.5200 0.6031
## test_mass_or_distributedmass 0.3369 0.4237 0.7951 0.4266
## mean_age:test_mass_or_distributedmass -0.0004 0.0004 -1.0993 0.2717
## ci.lb ci.ub
## intrcpt -0.0747 1.1533 .
## mean_age -0.0008 0.0004
## test_mass_or_distributedmass -0.4936 1.1673
## mean_age:test_mass_or_distributedmass -0.0012 0.0003
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1<36, young
ma_data_young_only %>%
mutate(age_months = mean_age/30.44) %>%
ggplot(aes(x = age_months, y = d_calc, size = n_1, color = test_mass_or_distributed)) +
geom_point() +
geom_smooth(method = "lm") +
ylab("Effect Size") +
xlab("Age (days)") +
ggtitle("Syntactical Bootstrapping effect size vs. Age (days), breakdown by test_mass_or_distributed, young only") 
m_age_tt_young <- rma.mv(d_calc ~ mean_age + test_mass_or_distributed, V = d_var_calc,
random = ~ 1 | short_cite, data = ma_data_young_only)
m_age_tt_interaction_young <- rma.mv(d_calc ~ mean_age * test_mass_or_distributed, V = d_var_calc,
random = ~ 1 | short_cite, data = ma_data_young_only)
summary(m_age_tt_young)##
## Multivariate Meta-Analysis Model (k = 57; method: REML)
##
## logLik Deviance AIC BIC AICc
## -114.3078 228.6157 236.6157 244.5716 237.4320
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2 0.3143 0.5607 17 no short_cite
##
## Test for Residual Heterogeneity:
## QE(df = 54) = 296.5626, p-val < .0001
##
## Test of Moderators (coefficients 2:3):
## QM(df = 2) = 0.3138, p-val = 0.8548
##
## Model Results:
##
## estimate se zval pval ci.lb
## intrcpt 0.5878 0.4247 1.3838 0.1664 -0.2447
## mean_age -0.0003 0.0005 -0.5162 0.6057 -0.0012
## test_mass_or_distributedmass 0.0487 0.3230 0.1508 0.8801 -0.5843
## ci.ub
## intrcpt 1.4203
## mean_age 0.0007
## test_mass_or_distributedmass 0.6817
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1summary(m_age_tt_interaction_young)##
## Multivariate Meta-Analysis Model (k = 57; method: REML)
##
## logLik Deviance AIC BIC AICc
## -113.2307 226.4615 236.4615 246.3130 237.7381
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2 0.3410 0.5840 17 no short_cite
##
## Test for Residual Heterogeneity:
## QE(df = 53) = 296.3776, p-val < .0001
##
## Test of Moderators (coefficients 2:4):
## QM(df = 3) = 1.3688, p-val = 0.7129
##
## Model Results:
##
## estimate se zval pval
## intrcpt -0.5465 1.1727 -0.4660 0.6412
## mean_age 0.0012 0.0015 0.8064 0.4200
## test_mass_or_distributedmass 1.3089 1.2612 1.0378 0.2993
## mean_age:test_mass_or_distributedmass -0.0016 0.0016 -1.0351 0.3006
## ci.lb ci.ub
## intrcpt -2.8450 1.7521
## mean_age -0.0017 0.0040
## test_mass_or_distributedmass -1.1629 3.7807
## mean_age:test_mass_or_distributedmass -0.0046 0.0014
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1>= 36, old
ma_data_old %>%
mutate(age_months = mean_age/30.44) %>%
ggplot(aes(x = age_months, y = d_calc, size = n_1, color = test_mass_or_distributed)) +
geom_point() +
ylab("Effect Size") +
xlab("Age (days)") +
ggtitle("Syntactical Bootstrapping effect size vs. Age (days), breakdown by test_mass_or_distributed, old only") 
repetition only
repetition only
ma_data %>%
ggplot(mapping = aes(x = n_repetitions_sentence, y = d_calc)) +
geom_point() +
geom_smooth(method = "lm") +
ylab("Effect Size") +
xlab("number of repetition per novel verb") +
ggtitle("Syntactical Bootstrapping effect size vs. number of repetitions for verb") +
theme_classic() +
theme(legend.position = "none")
m_rep <- rma.mv(d_calc ~ n_repetitions_sentence , V = d_var_calc,
random = ~ 1 | short_cite, data = ma_data)
summary(m_rep)##
## Multivariate Meta-Analysis Model (k = 79; method: REML)
##
## logLik Deviance AIC BIC AICc
## -162.0286 324.0571 330.0571 337.0886 330.3859
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2 0.2328 0.4825 22 no short_cite
##
## Test for Residual Heterogeneity:
## QE(df = 77) = 409.4680, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 1.6326, p-val = 0.2013
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.2566 0.1504 1.7059 0.0880 -0.0382 0.5514 .
## n_repetitions_sentence 0.0156 0.0122 1.2777 0.2013 -0.0083 0.0395
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1repetition only, young
ma_data_young %>%
ggplot(mapping = aes(x = n_repetitions_sentence, y = d_calc)) +
geom_point() +
geom_smooth(method = "lm") +
ylab("Effect Size") +
xlab("number of repetition per novel verb") +
ggtitle("Syntactical Bootstrapping effect size vs. number of repetitions for verb") +
theme_classic() +
theme(legend.position = "none")
m_rep_young <- rma.mv(d_calc ~ n_repetitions_sentence , V = d_var_calc,
random = ~ 1 | short_cite, data = ma_data_young)
summary(m_rep_young)##
## Multivariate Meta-Analysis Model (k = 57; method: REML)
##
## logLik Deviance AIC BIC AICc
## -115.0254 230.0508 236.0508 242.0727 236.5213
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2 0.3061 0.5533 17 no short_cite
##
## Test for Residual Heterogeneity:
## QE(df = 55) = 301.9470, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.4143, p-val = 0.5198
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.3186 0.1995 1.5974 0.1102 -0.0723 0.7095
## n_repetitions_sentence 0.0090 0.0140 0.6437 0.5198 -0.0184 0.0365
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1repetition only, old
ma_data_old %>%
ggplot(mapping = aes(x = n_repetitions_sentence, y = d_calc)) +
geom_point() +
geom_smooth(method = "lm") +
ylab("Effect Size") +
xlab("number of repetition per novel verb") +
ggtitle("Syntactical Bootstrapping effect size vs. number of repetitions for verb") +
theme_classic() +
theme(legend.position = "none")
m_rep_old <- rma.mv(d_calc ~ n_repetitions_sentence , V = d_var_calc,
random = ~ 1 | short_cite, data = ma_data_old)
summary(m_rep_old)##
## Multivariate Meta-Analysis Model (k = 22; method: REML)
##
## logLik Deviance AIC BIC AICc
## -41.3794 82.7587 88.7587 91.7459 90.2587
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2 0.0000 0.0000 6 no short_cite
##
## Test for Residual Heterogeneity:
## QE(df = 20) = 100.3330, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 6.9585, p-val = 0.0083
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.5495 0.0923 5.9557 <.0001 0.3687 0.7304
## n_repetitions_sentence -0.0460 0.0175 -2.6379 0.0083 -0.0802 -0.0118
##
## intrcpt ***
## n_repetitions_sentence **
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1repetition with age
correlation
all
m_rep_age <- rma.mv(d_calc ~ mean_age + n_repetitions_sentence , V = d_var_calc,
random = ~ 1 | short_cite, data = ma_data)
m_rep_age_interaction <- rma.mv(d_calc ~ mean_age*n_repetitions_sentence , V = d_var_calc,
random = ~ 1 | short_cite, data = ma_data)
summary(m_rep_age)##
## Multivariate Meta-Analysis Model (k = 79; method: REML)
##
## logLik Deviance AIC BIC AICc
## -159.4956 318.9913 326.9913 336.3142 327.5546
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2 0.2270 0.4764 22 no short_cite
##
## Test for Residual Heterogeneity:
## QE(df = 76) = 407.5030, p-val < .0001
##
## Test of Moderators (coefficients 2:3):
## QM(df = 2) = 6.2894, p-val = 0.0431
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.7339 0.2660 2.7585 0.0058 0.2124 1.2553 **
## mean_age -0.0004 0.0002 -2.1621 0.0306 -0.0008 -0.0000 *
## n_repetitions_sentence 0.0038 0.0133 0.2854 0.7753 -0.0223 0.0299
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1summary(m_rep_age_interaction)##
## Multivariate Meta-Analysis Model (k = 79; method: REML)
##
## logLik Deviance AIC BIC AICc
## -158.5885 317.1770 327.1770 338.7644 328.0466
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2 0.2139 0.4625 22 no short_cite
##
## Test for Residual Heterogeneity:
## QE(df = 75) = 394.6953, p-val < .0001
##
## Test of Moderators (coefficients 2:4):
## QM(df = 3) = 7.9638, p-val = 0.0468
##
## Model Results:
##
## estimate se zval pval ci.lb
## intrcpt 0.4284 0.3537 1.2114 0.2257 -0.2647
## mean_age -0.0000 0.0004 -0.0474 0.9622 -0.0007
## n_repetitions_sentence 0.0336 0.0266 1.2628 0.2067 -0.0186
## mean_age:n_repetitions_sentence -0.0000 0.0000 -1.2990 0.1939 -0.0001
## ci.ub
## intrcpt 1.1216
## mean_age 0.0007
## n_repetitions_sentence 0.0858
## mean_age:n_repetitions_sentence 0.0000
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1young, <36
m_rep_age_young <- rma.mv(d_calc ~ mean_age + n_repetitions_sentence , V = d_var_calc,
random = ~ 1 | short_cite, data = ma_data_young)
m_rep_age_interaction_young <- rma.mv(d_calc ~ mean_age*n_repetitions_sentence , V = d_var_calc,
random = ~ 1 | short_cite, data = ma_data_young)
summary(m_rep_age_young)##
## Multivariate Meta-Analysis Model (k = 57; method: REML)
##
## logLik Deviance AIC BIC AICc
## -114.6909 229.3817 237.3817 245.3376 238.1980
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2 0.3048 0.5521 17 no short_cite
##
## Test for Residual Heterogeneity:
## QE(df = 54) = 296.5847, p-val < .0001
##
## Test of Moderators (coefficients 2:3):
## QM(df = 2) = 0.5337, p-val = 0.7658
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.4734 0.4891 0.9681 0.3330 -0.4851 1.4320
## mean_age -0.0002 0.0005 -0.3465 0.7290 -0.0012 0.0008
## n_repetitions_sentence 0.0072 0.0149 0.4859 0.6271 -0.0220 0.0364
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1summary(m_rep_age_interaction_young)##
## Multivariate Meta-Analysis Model (k = 57; method: REML)
##
## logLik Deviance AIC BIC AICc
## -112.7315 225.4630 235.4630 245.3145 236.7396
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2 0.3118 0.5584 17 no short_cite
##
## Test for Residual Heterogeneity:
## QE(df = 53) = 293.9522, p-val < .0001
##
## Test of Moderators (coefficients 2:4):
## QM(df = 3) = 3.5403, p-val = 0.3156
##
## Model Results:
##
## estimate se zval pval ci.lb
## intrcpt -1.3840 1.1765 -1.1764 0.2395 -3.6899
## mean_age 0.0024 0.0016 1.5307 0.1258 -0.0007
## n_repetitions_sentence 0.1412 0.0786 1.7958 0.0725 -0.0129
## mean_age:n_repetitions_sentence -0.0002 0.0001 -1.7343 0.0829 -0.0004
## ci.ub
## intrcpt 0.9219
## mean_age 0.0056
## n_repetitions_sentence 0.2952 .
## mean_age:n_repetitions_sentence 0.0000 .
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1old > 36
m_rep_age_old <- rma.mv(d_calc ~ mean_age + n_repetitions_sentence , V = d_var_calc,
random = ~ 1 | short_cite, data = ma_data_old)
m_rep_age_interaction_old <- rma.mv(d_calc ~ mean_age*n_repetitions_sentence , V = d_var_calc,
random = ~ 1 | short_cite, data = ma_data_old)
summary(m_rep_age_old)##
## Multivariate Meta-Analysis Model (k = 22; method: REML)
##
## logLik Deviance AIC BIC AICc
## -41.2518 82.5035 90.5035 94.2813 93.3607
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2 0.0000 0.0000 6 no short_cite
##
## Test for Residual Heterogeneity:
## QE(df = 19) = 99.0894, p-val < .0001
##
## Test of Moderators (coefficients 2:3):
## QM(df = 2) = 8.2020, p-val = 0.0166
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 1.0411 0.4503 2.3119 0.0208 0.1585 1.9237 *
## mean_age -0.0003 0.0003 -1.1152 0.2648 -0.0009 0.0003
## n_repetitions_sentence -0.0515 0.0181 -2.8419 0.0045 -0.0871 -0.0160 **
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1summary(m_rep_age_interaction_old)##
## Multivariate Meta-Analysis Model (k = 22; method: REML)
##
## logLik Deviance AIC BIC AICc
## -40.8223 81.6445 91.6445 96.0964 96.6445
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2 0.0000 0.0000 6 no short_cite
##
## Test for Residual Heterogeneity:
## QE(df = 18) = 97.1759, p-val < .0001
##
## Test of Moderators (coefficients 2:4):
## QM(df = 3) = 10.1155, p-val = 0.0176
##
## Model Results:
##
## estimate se zval pval ci.lb
## intrcpt -1.4947 1.8876 -0.7918 0.4285 -5.1944
## mean_age 0.0017 0.0015 1.1273 0.2596 -0.0013
## n_repetitions_sentence 0.7744 0.5974 1.2964 0.1948 -0.3964
## mean_age:n_repetitions_sentence -0.0007 0.0005 -1.3833 0.1666 -0.0016
## ci.ub
## intrcpt 2.2050
## mean_age 0.0046
## n_repetitions_sentence 1.9453
## mean_age:n_repetitions_sentence 0.0003
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1vocab
vocab (median)
ma_data_with_vocab <- ma_data %>%
mutate(vocab = case_when(!is.na(productive_vocab_median) ~ productive_vocab_median,
!is.na(productive_vocab_mean) ~ productive_vocab_mean,
TRUE ~ NA_real_),
vocab_source = case_when(!is.na(productive_vocab_median) ~ "median",
!is.na(productive_vocab_mean) ~ "mean",
TRUE ~ NA_character_))
ma_data_with_vocab %>%
ggplot(aes(x = productive_vocab_median, y = d_calc)) +
geom_point() +
geom_smooth(method = "lm") +
theme_classic()
m_vocab <- rma.mv(d_calc ~ productive_vocab_median, V = d_var_calc,
random = ~ 1 | short_cite, data = ma_data_with_vocab)
summary(m_vocab)##
## Multivariate Meta-Analysis Model (k = 36; method: REML)
##
## logLik Deviance AIC BIC AICc
## -76.1570 152.3139 158.3139 162.8930 159.1139
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2 0.2602 0.5101 6 no short_cite
##
## Test for Residual Heterogeneity:
## QE(df = 34) = 160.5343, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 6.7042, p-val = 0.0096
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.8285 0.2557 3.2401 0.0012 0.3273 1.3297
## productive_vocab_median -0.0063 0.0024 -2.5892 0.0096 -0.0111 -0.0015
##
## intrcpt **
## productive_vocab_median **
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1age comparison
ma_data_with_vocab_age <- ma_data_with_vocab %>%
filter(vocab_source == "median" &
!is.na(vocab))
ma_data_with_vocab_age %>%
ggplot(aes(x = mean_age, y = d_calc)) +
geom_point() +
geom_smooth(method = "lm") +
theme_classic()
m_age_with_vocab <- rma.mv(d_calc ~ mean_age, V = d_var_calc,
random = ~ 1 | short_cite, data = ma_data_with_vocab_age)
summary(m_age_with_vocab)##
## Multivariate Meta-Analysis Model (k = 36; method: REML)
##
## logLik Deviance AIC BIC AICc
## -76.1407 152.2813 158.2813 162.8604 159.0813
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2 0.2767 0.5261 6 no short_cite
##
## Test for Residual Heterogeneity:
## QE(df = 34) = 161.6858, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 7.1130, p-val = 0.0077
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.9727 0.2867 3.3922 0.0007 0.4107 1.5347 ***
## mean_age -0.0006 0.0002 -2.6670 0.0077 -0.0010 -0.0002 **
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1< 36, young
ma_data_with_vocab_age_young <- ma_data_with_vocab_age %>%
mutate(age_months = mean_age/30.44) %>%
filter(age_months < 36)
ma_data_with_vocab_age_young %>%
ggplot(aes(x = productive_vocab_median, y = d_calc, size = n_1)) +
geom_point() +
geom_smooth(method = "lm") +
ylab("Effect Size") +
xlab("Age (days)") +
ggtitle("Syntactical Bootstrapping effect size vs. vocab (months)") 
CATEGORICAL_VARS <- c("sentence_structure", "agent_argument_type", "patient_argument_type", "stimuli_actor",
"presentation_type", "character_identification",
"test_mass_or_distributed", "practice_phase", "test_method")
ma_data %>% filter(!is.na(mean_age)) %>% summarize(mean_age = mean(mean_age)) ## # A tibble: 1 x 1
## mean_age
## <dbl>
## 1 907.ma_data_ageExpl <- ma_data %>% mutate(AGE_group = (if_else(mean_age > 915.22, "OLD", "YOUNG")))
ggplot(ma_data_ageExpl, aes(x = sentence_structure,
y = d_calc,
group = presentation_type,
color = presentation_type
)) +
geom_line() +
geom_point(position = "jitter") +
geom_point(alpha = .4) +
ylab("Effect Size") +
xlab("Sentence Structure") +
ggtitle("ME effect size by Sentence Structure") 
ggplot(ma_data_ageExpl, aes(x = sentence_structure,
y = d_calc,
group = unique_id,
color = unique_id
)) +
geom_line() +
geom_point(position = "jitter") +
geom_point(alpha = .4) +
ylab("Effect Size") +
xlab("Sentence Structure") +
ggtitle("ME effect size by Sentence Structure") 
ma_data %>%
group_by(agent_argument_type_clean) %>% count## # A tibble: 4 x 2
## # Groups: agent_argument_type_clean [4]
## agent_argument_type_clean n
## <chr> <int>
## 1 noun 21
## 2 noun_phrase 9
## 3 pronoun 23
## 4 varying_agent 26ggplot(ma_data, aes(x = agent_argument_type_clean,
y = d_calc,
color = agent_argument_type_clean)) +
geom_violin() +
geom_point(alpha = .4) +
ylab("Effect Size") +
xlab("Response Mode") +
ggtitle("ME effect size by Response mode") +
theme_classic() +
theme(legend.position = "none")
cis_by_aa <- ma_data %>%
group_by(agent_argument_type_clean) %>%
summarize(mean = mean(d_calc),
sd = sd(d_calc),
n = n()) %>%
mutate(ci_range_95 = 1.96 * (sd/sqrt(n)),
ci_lower = mean - ci_range_95,
ci_upper = mean + ci_range_95)
#pdf("plots/moderator_plot1.pdf", height = 6, width = 6)
ggplot(ma_data, aes(x = agent_argument_type_clean,
y = d_calc,
color = agent_argument_type_clean)) +
geom_violin() +
geom_point(alpha = .4) +
ylab("Effect Size") +
xlab("Response Mode") +
ggtitle("ME effect size by Response mode") +
geom_pointrange(data = cis_by_aa,
aes(x = agent_argument_type_clean,
y = mean, ymin = ci_lower,
ymax = ci_upper),
color = "black") +
geom_hline(aes(yintercept = 0), linetype = 2) +
theme_classic(base_size = 16) +
theme(legend.position = "none")
< 36, young, age comparison
ma_data_with_vocab_age_young %>%
ggplot(aes(x = age_months, y = d_calc, size = n_1)) +
geom_point() +
geom_smooth(method = "lm") +
ylab("Effect Size") +
xlab("Age (days)") +
ggtitle("Syntactical Bootstrapping effect size vs. Age (months)") 
m_age_with_vocab_young <- rma.mv(d_calc ~ mean_age, V = d_var_calc,
random = ~ 1 | short_cite, data = ma_data_with_vocab_age_young)
summary(m_age_with_vocab)##
## Multivariate Meta-Analysis Model (k = 36; method: REML)
##
## logLik Deviance AIC BIC AICc
## -76.1407 152.2813 158.2813 162.8604 159.0813
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2 0.2767 0.5261 6 no short_cite
##
## Test for Residual Heterogeneity:
## QE(df = 34) = 161.6858, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 7.1130, p-val = 0.0077
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.9727 0.2867 3.3922 0.0007 0.4107 1.5347 ***
## mean_age -0.0006 0.0002 -2.6670 0.0077 -0.0010 -0.0002 **
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1correlationt est
cor.test(ma_data_with_vocab$mean_age,
ma_data_with_vocab$productive_vocab_median)##
## Pearson's product-moment correlation
##
## data: ma_data_with_vocab$mean_age and ma_data_with_vocab$productive_vocab_median
## t = 11.46, df = 34, p-value = 3.181e-13
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.7957418 0.9435289
## sample estimates:
## cor
## 0.8912725